基于马尔科夫专家场的泊松噪声图像去噪方法

提出一种基于贝叶斯概率模型的泊松噪声图像去噪方法. 该方法基于贝叶斯最大后验概率模型框架，结合泊松概率分布，构建图像去噪模型. 考虑到马尔科夫随机场不能对复杂自然图像有效表征，引入高阶的马尔科夫专家场作为模型先验正则项，以表征图像自身概率分布. 利用二次惩罚函数，优化求解去噪模型，还原清晰图像. 将所提方法与其他去噪算法进行仿真实验对比，并采用峰值信噪比和结构相似性2种评价指标对去噪效果进行客观评价. 实验结果表明：与传统去噪方法相比，该方法的峰值信噪比至少提升了0.18 dB，去噪性能显著优于其他方法，能更好地保留图像的细节信息.

Image Poisson denoising algorithm based on Markov fields of experts

A Poisson noise image denoising method based on Bayesian probability model was proposed. An image denoising model was constructed based on Bayesian maximum a posteriori probability model and with combination of Poisson probability distribution. Considering that Markov random fields cannot represent complex natural images effectively, a higher-order Markov fields of experts was introduced as a prior regular term of the model to represent the probability distribution of the image. The quadratic penalty function was used to optimize the denoising model and restore clear images. The proposed method was compared with other denoising algorithms; the denoising effect was evaluated objectively by using two evaluation indexes: peak signal-to-noise ratio and structural similarity. The experimental results show that, compared with the traditional denoising methods, the peak signal-to-noise ratio of this method increased by at least 0.18 dB, and the denoising performance is significantly better than that of other methods. Thus, the details of the image can be retained better by using this mothed.